cleaver

cleaver simulates enzymatic cleavage of polypeptide sequences to predict proteolytic cleavage sites and support analysis of protein structure, functional domains, and post-translational processing.


Key Features:

  • In-silico Simulation: Performs virtual cleavage of polypeptide sequences by modeling proteolytic enzyme activity.
  • Predictive Analysis: Predicts potential cleavage sites on amino acid sequences based on known enzyme specificity patterns to aid mapping of protein domains and post-translational modification sites.
  • Interdisciplinary Application: Provides computational predictions of enzymatic processing relevant to proteomics, genomics, and molecular biology analyses.

Scientific Applications:

  • Protein Structure Analysis: Identifies cleavage sites to inform structural organization and domain boundaries in proteins.
  • Functional Domain Mapping: Pinpoints potential functional regions within polypeptides by locating protease-sensitive sites.
  • Post-translational Modification Studies: Explores how enzymatic processing may affect protein activity and stability through predicted cleavage events.
  • Proteomics: Facilitates identification of protein fragments resulting from enzymatic digestion in proteomic studies.
  • Molecular Biology: Provides insights into protein processing mechanisms relevant to molecular biology research.
  • Genomics: Links sequence information with potential functional outcomes by mapping proteolytic cleavage sites to genomic-derived polypeptide sequences.

Methodology:

Uses computational algorithms to analyze amino acid sequences and simulate cleavage by proteolytic enzymes, predicting cut sites based on known enzyme specificity patterns and sequence context.

Topics

Collections

Details

License:
GPL-3.0
Tool Type:
command-line tool, library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
1/17/2017
Last Updated:
11/25/2024

Operations

Publications

Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods. 2015;12(2):115-121. doi:10.1038/nmeth.3252. PMID:25633503. PMCID:PMC4509590.

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